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Half-Empty or Half-Full? A Hybrid Approach to Predict Recycling Behavior of Consumers to Increase Reverse Vending Machine Uptime

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Exploring Service Science (IESS 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 377))

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Abstract

Reverse Vending Machines (RVMs) are a proven instrument for facilitating closed-loop plastic packaging recycling. A good customer experience at the RVM is crucial for a further proliferation of this technology. Bin full events are the major reason for Reverse Vending Machine (RVM) downtime at the world leader in the RVM market. The paper at hand develops and evaluates an approach based on machine learning and statistical approximation to foresee bin full events and, thus increase uptime of RVMs. Our approach relies on forecasting the hourly time series of returned beverage containers at a given RVM. We contribute by developing and evaluating an approach for hourly forecasts in a retail setting – this combination of application domain and forecast granularity is novel. A trace-driven simulation confirms that the forecasting-based approach leads to less downtime and costs than naïve emptying strategies.

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Authors and Affiliations

  1. Karlsruhe Service Research Institute, Karlsruhe Institute of Technology, Kaiserstraße 12, 76131, Karlsruhe, Germany

    Jannis Walk, Robin Hirt & Niklas Kühl

  2. TOMRA Systems ASA, Drengsrudhagen 2, 1385, Asker, Norway

    Erik R. Hersløv

Authors
  1. Jannis Walk
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  2. Robin Hirt
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  3. Niklas Kühl
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  4. Erik R. Hersløv
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Corresponding author

Correspondence to Jannis Walk .

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Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Henriqueta Nóvoa

  2. University Politehnica of Bucharest, Bucharest, Romania

    Monica Drăgoicea

  3. Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

    Niklas Kühl

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Walk, J., Hirt, R., Kühl, N., Hersløv, E.R. (2020). Half-Empty or Half-Full? A Hybrid Approach to Predict Recycling Behavior of Consumers to Increase Reverse Vending Machine Uptime. In: Nóvoa, H., Drăgoicea, M., Kühl, N. (eds) Exploring Service Science. IESS 2020. Lecture Notes in Business Information Processing, vol 377. Springer, Cham. https://doi.org/10.1007/978-3-030-38724-2_8

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  • DOI: https://doi.org/10.1007/978-3-030-38724-2_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-38723-5

  • Online ISBN: 978-3-030-38724-2

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